Abstract

In this paper, covariance matrices are exploited to encode the deepconvolutional neural networks (DCNN) features for facial expressionrecognition. The space geometry of the covariance matrices is that of SymmetricPositive Definite (SPD) matrices. By performing the classification of thefacial expressions using Gaussian kernel on SPD manifold, we show that thecovariance descriptors computed on DCNN features are more efficient than thestandard classification with fully connected layers and softmax. Byimplementing our approach using the VGG-face and ExpNet architectures withextensive experiments on the Oulu-CASIA and SFEW datasets, we show that theproposed approach achieves performance at the state of the art for facialexpression recognition.